BEHRT: Transformer for Electronic Health Records
Indexed incrossrefdoajpubmed
Abstract
Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers with the chance of early intervention, better disease management, and efficient allocation of healthcare resources. The latest developments in machine learning (including deep learning) provides a great opportunity to address this unmet need. In this study, we introduce BEHRT: A deep neural sequence transduction model for electronic health records (EHR), capable of simultaneously predicting the likelihood of 301 conditions in one's future…
Citation impact
516
total citations
- FWCI
- 32.37
- Percentile
- 100%
- References
- 42
Citations per year
Authors
9Topics & keywords
Keywords
- Health records
- Medical diagnosis
- Artificial intelligence
- Computer science
- Machine learning
- Deep learning
- Scalability
- Electronic health record
No related works found for this paper.